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Zhang S, Yuan Y, Wang Z, Li J. The application of laser‑induced fluorescence in oil spill detection. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:23462-23481. [PMID: 38466385 DOI: 10.1007/s11356-024-32807-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 03/03/2024] [Indexed: 03/13/2024]
Abstract
Over the past two decades, oil spills have been one of the most serious ecological disasters, causing massive damage to the aquatic and terrestrial ecosystems as well as the socio-economy. In view of this situation, several methods have been developed and utilized to analyze oil samples. Among these methods, laser-induced fluorescence (LIF) technology has been widely used in oil spill detection due to its classification method, which is based on the fluorescence characteristics of chemical material in oil. This review systematically summarized the LIF technology from the perspective of excitation wavelength selection and the application of traditional and novel machine learning algorithms to fluorescence spectrum processing, both of which are critical for qualitative and quantitative analysis of oil spills. It can be seen that an appropriate excitation wavelength is indispensable for spectral discrimination due to different kinds of polycyclic aromatic hydrocarbons' (PAHs) compounds in petroleum products. By summarizing some articles related to LIF technology, we discuss the influence of the excitation wavelength on the accuracy of the oil spill detection model and proposed several suggestions on the selection of excitation wavelength. In addition, we introduced some traditional and novel machine learning (ML) algorithms and discussed the strengths and weaknesses of these algorithms and their applicable scenarios. With an appropriate excitation wavelength and data processing algorithm, it is believed that laser-induced fluorescence technology will become an efficient technique for real-time detection and analysis of oil spills.
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Affiliation(s)
- Shubo Zhang
- Department of Optical Science and Engineering, Fudan University, Shanghai, 200433, China
| | - Yafei Yuan
- Department of Sports Media and Information Technology, Shandong Sport University, Jinan, 250102, Shandong, China.
| | - Zhanhu Wang
- Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
| | - Jing Li
- Department of Optical Science and Engineering, Fudan University, Shanghai, 200433, China
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Ding Y, Sun Q, Lin Y, Ping Q, Peng N, Wang L, Li Y. Application of artificial intelligence in (waste)water disinfection: Emphasizing the regulation of disinfection by-products formation and residues prediction. WATER RESEARCH 2024; 253:121267. [PMID: 38350192 DOI: 10.1016/j.watres.2024.121267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 01/30/2024] [Accepted: 02/04/2024] [Indexed: 02/15/2024]
Abstract
Water/wastewater ((waste)water) disinfection, as a critical process during drinking water or wastewater treatment, can simultaneously inactivate pathogens and remove emerging organic contaminants. Due to fluctuations of (waste)water quantity and quality during the disinfection process, conventional disinfection models cannot handle intricate nonlinear situations and provide immediate responses. Artificial intelligence (AI) techniques, which can capture complex variations and accurately predict/adjust outputs on time, exhibit excellent performance for (waste)water disinfection. In this review, AI application data within the disinfection domain were searched and analyzed using CiteSpace. Then, the application of AI in the (waste)water disinfection process was comprehensively reviewed, and in addition to conventional disinfection processes, novel disinfection processes were also examined. Then, the application of AI in disinfection by-products (DBPs) formation control and disinfection residues prediction was discussed, and unregulated DBPs were also examined. Current studies have suggested that among AI techniques, fuzzy logic-based neuro systems exhibit superior control performance in (waste)water disinfection, while single AI technology is insufficient to support their applications in full-scale (waste)water treatment plants. Thus, attention should be paid to the development of hybrid AI technologies, which can give full play to the characteristics of different AI technologies and achieve a more refined effectiveness. This review provides comprehensive information for an in-depth understanding of AI application in (waste)water disinfection and reducing undesirable risks caused by disinfection processes.
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Affiliation(s)
- Yizhe Ding
- State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Yangtze River Water Environment, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China
| | - Qiya Sun
- State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Yangtze River Water Environment, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China
| | - Yuqian Lin
- State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Yangtze River Water Environment, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China
| | - Qian Ping
- State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Yangtze River Water Environment, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, PR China
| | - Nuo Peng
- State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Yangtze River Water Environment, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China
| | - Lin Wang
- State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Yangtze River Water Environment, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, PR China.
| | - Yongmei Li
- State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Yangtze River Water Environment, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, PR China
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Fu H, Kang Q, Sun X, Liu W, Li Y, Chen B, Zhang B, Bao M. Mechanism of nearshore sediment-facilitated oil transport: New insights from causal inference analysis. JOURNAL OF HAZARDOUS MATERIALS 2024; 465:133187. [PMID: 38104519 DOI: 10.1016/j.jhazmat.2023.133187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 11/12/2023] [Accepted: 12/04/2023] [Indexed: 12/19/2023]
Abstract
A quantitative understanding of spilled oil transport in a nearshore environment is challenging due to the complex physicochemical processes in aqueous conditions. The physicochemical processes involved in oil sinking mainly include oil dispersion, sediment settling, and oil-sediment interaction. For the first time, this work attempts to address the sinking mechanism in petroleum contaminant transport using structural causal models based on observed data. The effects of nearshore salinity distribution from the estuary to the ocean on those three processes are examined. The causal inference reveals sediment settling is the crucial process for oil sinking. Salinity indirectly affects oil sinking by promoting sediment settling rather than directly affecting oil-sediment interaction. The increase of salinity from 0‰ to 35‰ provides a natural enhancement for sediment settling. Notably, unbiased causal effect estimates demonstrate the strongest positive causal effect on the settling efficiency of sediments is posed by increasing oil dispersion effectiveness, with a normalized value of 1.023. The highest strength of the causal relationship between oil dispersion and sediment settling highlights the importance of the dispersing characteristics of spilled oil to sediment-facilitated oil transport. The employed logic, a data-driven method, will shed light on adopting advanced causal inference tools to unravel the complicated contaminants' transport.
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Affiliation(s)
- Hongrui Fu
- Frontiers Science Center for Deep Ocean Multispheres and Earth System, and Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education, Ocean University of China, Qingdao 266100, China; College of Chemistry and Chemical Engineering, Ocean University of China, Qingdao 266100, China
| | - Qiao Kang
- The Northern Region Persistent Organic Pollution (NRPOP) Control Laboratory, Faculty of Engineering and Applied Science, Memorial University, St. John's, NL A1B 3×5, Canada
| | - Xiaojun Sun
- Frontiers Science Center for Deep Ocean Multispheres and Earth System, and Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education, Ocean University of China, Qingdao 266100, China; College of Chemistry and Chemical Engineering, Ocean University of China, Qingdao 266100, China
| | - Wei Liu
- Frontiers Science Center for Deep Ocean Multispheres and Earth System, and Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education, Ocean University of China, Qingdao 266100, China; College of Chemistry and Chemical Engineering, Ocean University of China, Qingdao 266100, China
| | - Yang Li
- China Petrochemical Corporation (Sinopec Group), Beijing 100728, China
| | - Bing Chen
- The Northern Region Persistent Organic Pollution (NRPOP) Control Laboratory, Faculty of Engineering and Applied Science, Memorial University, St. John's, NL A1B 3×5, Canada
| | - Baiyu Zhang
- The Northern Region Persistent Organic Pollution (NRPOP) Control Laboratory, Faculty of Engineering and Applied Science, Memorial University, St. John's, NL A1B 3×5, Canada
| | - Mutai Bao
- Frontiers Science Center for Deep Ocean Multispheres and Earth System, and Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education, Ocean University of China, Qingdao 266100, China; College of Chemistry and Chemical Engineering, Ocean University of China, Qingdao 266100, China.
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Faybishenko B, Bakhtavar E, Hewage K, Sadiq R. Chemical composition of arsenic-based acid mine drainage in the downstream of a gold mine: Fuzzy regression and clustering analysis. JOURNAL OF HAZARDOUS MATERIALS 2024; 465:133250. [PMID: 38157814 DOI: 10.1016/j.jhazmat.2023.133250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 12/04/2023] [Accepted: 12/11/2023] [Indexed: 01/03/2024]
Abstract
This study employs fuzzy regression and fuzzy multivariate clustering techniques to analyze arsenic-polluted water samples originating from acid rock drainage in waste rock dumps. The research focuses on understanding the complex relationships between variables associated with arsenic contamination, such as water arsenic concentration, pH levels, and soil characteristics. To this end, fuzzy regression models were developed to estimate the relationships between water arsenic concentration and independent variables, thus, incorporating the inherent uncertainties into the analysis. Furthermore, multivariate fuzzy k-means clustering analysis facilitated the identification of fuzzy-based clusters within the dataset, providing insights into spatial patterns and potential sources of arsenic pollution. The pairwise comparisons indicated the strongest correlation of 0.62 between soil total arsenic and pH, while the weakest correlation of 0.13 was observed between soil-soluble arsenic and soil iron, providing valuable insights into their relationships and impact on water arsenic levels. The associated uncertainties in the relationships among the variables were determined based on the degree of belongingness of each data point to various fuzzy sets. Three distinct clusters emerged from the analysis: Cluster 1 comprised Points 5, 6, and 7; Cluster 2 included Points 1, 2, 3, 4, 8, and 9; and Cluster 3 consisted of Points 10, 11, 12, and 13. The findings enhance our understanding of the factors influencing arsenic contamination to provide an effective mitigation strategy in acid rock drainage scenarios. This research also demonstrates the applicability and effectiveness of fuzzy regression and fuzzy multivariate clustering in the analysis of arsenic-polluted water samples.
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Affiliation(s)
- Boris Faybishenko
- Energy Geosciences Division, Earth & Environmental Sciences Area, Lawrence Berkeley National Laboratory, University of California, Berkeley, USA
| | - Ezzeddin Bakhtavar
- Faculty of Environment, Urmia University of Technology, Urmia 5716617165, Iran; School of Engineering, University of British Columbia, Okanagan, Kelowna V1V 1V7, BC, Canada.
| | - Kasun Hewage
- School of Engineering, University of British Columbia, Okanagan, Kelowna V1V 1V7, BC, Canada
| | - Rehan Sadiq
- School of Engineering, University of British Columbia, Okanagan, Kelowna V1V 1V7, BC, Canada
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Dong Q, Li Y, Wei X, Jiao L, Wu L, Dong Z, An Y. A city-level dataset of heavy metal emissions into the atmosphere across China from 2015-2020. Sci Data 2024; 11:258. [PMID: 38424081 PMCID: PMC10904851 DOI: 10.1038/s41597-024-03089-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 02/21/2024] [Indexed: 03/02/2024] Open
Abstract
The absence of nationwide distribution data regarding heavy metal emissions into the atmosphere poses a significant constraint in environmental research and public health assessment. In response to the critical data deficiency, we have established a dataset covering Cr, Cd, As, and Pb emissions into the atmosphere (HMEAs, unit: ton) across 367 municipalities in China. Initially, we collected HMEAs data and covariates such as industrial emissions, vehicle emissions, meteorological variables, among other ten indicators. Following this, nine machine learning models, including Linear Regression (LR), Ridge, Bayesian Ridge (Bayesian), K-Neighbors Regressor (KNN), MLP Regressor (MLP), Random Forest Regressor (RF), LGBM Regressor (LGBM), Lasso, and ElasticNet, were assessed using coefficient of determination (R2), root-mean-square error (RMSE) and Mean Absolute Error (MAE) on the testing dataset. RF and LGBM models were chosen, due to their favorable predictive performance (R2: 0.58-0.84, lower RMSE/MAE), confirming their robustness in modelling. This dataset serves as a valuable resource for informing environmental policies, monitoring air quality, conducting environmental assessments, and facilitating academic research.
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Affiliation(s)
- Qi Dong
- Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin, 300071, China
- Xiangtan Experimental Station of Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Xiangtan, Hunan, 411199, China
| | - Yue Li
- College of Computer Science, Nankai University, Tianjin, 300350, China
| | - Xinhua Wei
- College of Computer Science, Nankai University, Tianjin, 300350, China
| | - Le Jiao
- Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin, 300071, China
| | - Lina Wu
- Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin, 300071, China
| | - Zexin Dong
- Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin, 300071, China
| | - Yi An
- Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin, 300071, China.
- Xiangtan Experimental Station of Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Xiangtan, Hunan, 411199, China.
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Hu G, Mian HR, Mohammadiun S, Rodriguez MJ, Hewage K, Sadiq R. Appraisal of machine learning techniques for predicting emerging disinfection byproducts in small water distribution networks. JOURNAL OF HAZARDOUS MATERIALS 2023; 446:130633. [PMID: 36610346 DOI: 10.1016/j.jhazmat.2022.130633] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/14/2022] [Accepted: 12/17/2022] [Indexed: 06/17/2023]
Abstract
Monitoring emerging disinfection byproducts (DBPs) is challenging for many small water distribution networks (SWDNs), and machine learning-based predictive modeling could be an alternative solution. In this study, eleven machine learning techniques, including three multivariate linear regression-based, three regression tree-based, three neural networks-based, and two advanced non-parametric regression techniques, are used to develop models for predicting three emerging DBPs (dichloroacetonitrile, chloropicrin, and trichloropropanone) in SWDNs. Predictors of the models include commonly-measured water quality parameters and two conventional DBP groups. Sampling data of 141 cases were collected from eleven SWDNs in Canada, in which 70 % were randomly selected for model training and the rest were used for validation. The modeling process was reiterated 1000 times for each model. The results show that models developed using advanced regression techniques, including support vector regression and Gaussian process regression, exhibited the best prediction performance. Support vector regression models showed the highest prediction accuracy (R2 =0.94) and stability for predicting dichloroacetonitrile and trichloropropanone, and Gaussian process regression models are optimal for predicting chloropicrin (R2 =0.92). The difference is likely due to the much lower concentrations of chloropicrin than dichloroacetonitrile and trichloropropanone. Advanced non-parametric regression techniques, characterized by a probabilistic nature, were identified as most suitable for developing the predictive models, followed by neural network-based (e.g., generalized regression neural network), regression tree-based (e.g., random forest), and multivariate linear regression-based techniques. This study identifies promising machine learning techniques among many commonly-used alternatives for monitoring emerging DBPs in SWDNs under data constraints.
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Affiliation(s)
- Guangji Hu
- School of Environmental Science and Engineering, Qingdao University, Qingdao, Shandong 266071, China; School of Engineering, University of British Columbia Okanagan, 3333 University Way, Kelowna, British Columbia, V1V 1V7, Canada.
| | - Haroon R Mian
- School of Engineering, University of British Columbia Okanagan, 3333 University Way, Kelowna, British Columbia, V1V 1V7, Canada.
| | - Saeed Mohammadiun
- School of Engineering, University of British Columbia Okanagan, 3333 University Way, Kelowna, British Columbia, V1V 1V7, Canada
| | - Manuel J Rodriguez
- École Supérieure D'aménagement du Territoire et Développement Régional (ESAD), 2325, allée des Bibliothèque Université Laval, Québec City, QC G1V 0A6, Canada
| | - Kasun Hewage
- School of Engineering, University of British Columbia Okanagan, 3333 University Way, Kelowna, British Columbia, V1V 1V7, Canada
| | - Rehan Sadiq
- School of Engineering, University of British Columbia Okanagan, 3333 University Way, Kelowna, British Columbia, V1V 1V7, Canada
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